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REGRESSION Description also available in video format (attached below), for better experience use your desktop. Introduction ·       Regression analysis refers to assessing the relationship between the outcome variable and one or more variables. The outcome variable is known as the dependent or response variable and the risk elements,  and co-founders  are known as predictors or independent variables. The dependent variable is shown by “y” and independent variables are shown by “x” in regression analysis. ·     The sample of a correlation coefficient is estimated in the correlation analysis. It ranges between -1 and +1, denoted by r and quantifies the strength and direction of the linear association among two variables. The correlation among two variables can either be positive, i.e. a higher level of one variable is related to a higher level of another or negative, i.e. a higher level of one variable is related to a lower level of the other. ·    The sign of the coefficient

  CORRELATION Description also available in video format (attached below), for better experience use your desktop. Introduction ·        It refers to a process which establishes a relation between two variables ·        After developing correlation you get an idea about whether the two variables are related or not ·        Correlation coefficient is generally represent by the symbol ( r) and usually ranges from -1 to +1 ·        When the coefficient is close to -1, it is called negative relationship between the two variables. ·        When the coefficient is close to +1, it is called positive relationship between the two variables.   Scatter Diagram ·        It is used to examine the relationship between the X & Y axis with one variable ·        If all the points in the diagram stretch in one line then it means that the correlation is perfect ·        If all the points are scattering widely then it means that the correlation is low ·        If the scatter